1. Field of the Invention
The invention relates to imaging devices such as cameras, video cameras, microscopes, and other visualization techniques, and more particularly, to the acquisition of images and video using fewer measurements than previous techniques.
2. Brief Description of the Related Art
The large amount of raw data acquired in a conventional digital image or video often necessitates immediate compression in order to store or transmit that data. This compression typically exploits a priori knowledge, such as the fact that an N-pixel image can be well approximated as a sparse linear combination of K<<N wavelets. These appropriate wavelet coefficients can be efficiently computed from the N pixel values and then easily stored or transmitted along with their locations. Similar procedures are applied to videos containing F frames of P pixels each; we let N=FP denote the number of video “voxels”.
This process has two major shortcomings. First, acquiring large amounts of raw image or video data (large N) can be expensive, particularly at wavelengths where CMOS or CCD sensing technology is limited. Second, compressing raw data can be computationally demanding, particularly in the case of video. While there may appear to be no way around this procedure of “sample, process, keep the important information, and throw away the rest,” a new theory known as Compressive Sensing (CS) has emerged that offers hope for directly acquiring a compressed digital representation of a signal without first sampling that signal. See Candès, E., Romberg, J., Tao, T., “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory 52 (2006) 489-509; David Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, Volume 52, Issue 4, April 2006, Pages: 1289-1306; and Candès, E., Tao, T., “Near optimal signal recovery from random projections and universal encoding strategies,” (2004) Preprint.
Traditional methods of conserving power in camera monitoring and surveillance applications have either relied upon scheduling sleeping and awake modes, or supplementary sensors such as infrared motion detectors to decide when to power on the camera. In the former case, scheduled power-off periods could result in missing an important event entirely. In the latter case, we require additional hardware that may be costly or undesirable. Moreover, in both cases the system suffers from a “power-on lag,” which delays image or video capture, potentially causing the camera to miss the important event. These problems would be solved by allowing the camera to continuously monitor the scene in a low-power, low-rate mode, and by enabling it to immediately increase its rate when an important or interesting event occurs. This kind of scheme is impossible in the traditional digital camera paradigm, which is an all-or-nothing scheme: either an image/video is captured at full rate, or no image/video is captured at all. Thus a camera that can continuously monitor at low-rate and increase to full rate with no lag-time is not found in the art, but is directly enabled by our unique camera architecture.
Other efforts on compressed imaging include Pitsianis, N. P., Brady, D. J., Sun, X.: “Sensor-layer image compression based on the quantized cosine transform,” SPIE Visual Information Processing XIV (2005) and Brady, D. J., Feldman, M., Pitsianis, N., Guo, J. P., Portnoy, A., Fiddy, M., “Compressive optical MONTAGE photography,” SPIE Photonic Devices and Algorithms for Computing VII (2005), which employ optical elements to perform transform coding of multispectral images. The hardware designed for these purposes uses concepts that include optical projections, group testing (see Cormode, G., Muthukrishnan, S., “Towards an algorithmic theory of compressed sensing,” DIMACS Tech. Report 2005-40 (2005)), and signal inference. Two notable previous DMD-driven applications involve confocal microscopy (Lane, P. M., Elliott, R. P., MacAulay, C. E., “Confocal microendoscopy with chromatic sectioning,” Proc. SPIE. Volume 4959 (2003) 23-26) and micro-optoelectromechanical (MOEM) systems (DeVerse, R. A., Coifman, R. R., Coppi, A. C., Fateley, W. G., Geshwind, F., Hammaker, R. M., Valenti, S., Warner, F. J., “Application of spatial light modulators for new modalities in spectrometry and imaging,” Proc. SPIE. Volume 4959 (2003)).
The present invention overcomes shortcomings of the prior approaches. Preferred embodiments of the present invention take fewer measurements than prior techniques, enable significant reduction in the resources (power, computation) required for visualization and use only a small number of physical sensors. The reduction in the size of the hardware associated with preferred embodiments of the invention further may significantly reduce costs of visualization systems. The present invention can also acquire and process streaming video data (time-varying images). Finally, the present invention can adjust its data acquisition rate according to the amount of activity in the scene it is imaging.